The use of regularization to improve prediction is well established and was indeed studied already by [1]} in the context of ill-posed inverse problems. Likewise, regularization for the benefit of prescription was proposed by [2]} and has been tremendously influential. Decision formulations and their associated prescriptions can be guarded against overfitting by considering regularized cost estimators.
Such regularized formulations estimate the cost of any decision as
\(\mathbb {E}_{\hat{\mathbb {P}}_T}(\ell (x, \xi )) + R(x, \hat{\mathbb {P}}_T, T),\) 
